AutoGineer
Generated banner for Notion Knowledge Capture
Generated logo for Notion Knowledge Capture

Notion Knowledge Capture

JetBrainsOfficialCreated by OpenAIDistributed by JetBrains

  • Skill
  • Tool integration
  • Automation and integrations
  • MIT
  • No issues found
Download ZIP

From the author

Capture conversations and decisions into structured Notion pages; use when turning chats/notes into wiki entries, how-tos, decisions, or FAQs with proper linking.
— the description by OpenAI

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a systematic approach to transforming ephemeral chat logs and notes into durable, structured knowledge within Notion. It guides the user through defining the content type, identifying the appropriate database, extracting key information, and creating or updating pages with proper metadata and cross-linking. By leveraging predefined templates and database schemas, it ensures that team wikis, decision logs, and how-to guides remain consistent and discoverable.

What it does

  • Converts unstructured conversation data into structured Notion pages.
  • Supports multiple content types including decisions, FAQs, how-tos, and wiki entries.
  • Facilitates the creation and updating of Notion pages using specific database schemas.
  • Ensures proper linking and relationship management between new and existing documentation.
  • Provides a clear setup procedure for Notion integration via MCP.

When to use it

  • When you need to document a decision made during a team chat.
  • When converting a series of troubleshooting steps into a formal how-to guide.
  • When building a team wiki from recurring questions or project notes.
  • When you need to update existing documentation with new context or status changes.

Advantages

  • Reduces knowledge loss by formalizing informal discussions.
  • Maintains consistency across documentation using standardized templates.
  • Improves discoverability through structured tagging and relational linking.
  • Integrates directly with Notion databases for seamless knowledge management.

Usage examples

“Capture our recent decision about the API migration into the decision log.”
The agent will extract the rationale and outcome from the chat, locate the decision log database, and create a structured page with the appropriate properties.
“Turn this troubleshooting conversation into a how-to guide for the team wiki.”
The agent will structure the steps, prerequisites, and edge cases into a template-based how-to page and link it to relevant project documentation.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/jetbrains/notion-knowledge-capture/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip notion-knowledge-capture.zip -d .agents/skills

Documentation by OpenAI

Knowledge Capture

Convert conversations and notes into structured, linkable Notion pages for easy reuse.

Quick start

  1. Clarify what to capture (decision, how-to, FAQ, learning, documentation) and target audience.

  2. Identify the right database/template in reference/ (team wiki, how-to, FAQ, decision log, learning, documentation).

  3. Pull any prior context from Notion with Notion:notion-search → Notion:notion-fetch (existing pages to update/link).

  4. Draft the page with Notion:notion-create-pages using the database’s schema; include summary, context, source links, and tags/owners.

  5. Link from hub pages and related records; update status/owners with Notion:notion-update-page as the source evolves.

Workflow

0) If any MCP call fails because Notion MCP is not connected, pause and set it up:

  1. Add the Notion MCP:

    • codex mcp add notion --url https://mcp.notion.com/mcp

  2. Enable remote MCP client:

    • Set [features].rmcp_client = true in config.toml or run codex --enable rmcp_client

  3. Log in with OAuth:

    • codex mcp login notion

After successful login, the user will have to restart codex. You should finish your answer and tell them so when they try again they can continue with Step 1.

1) Define the capture

  • Ask purpose, audience, freshness, and whether this is new or an update.

  • Determine content type: decision, how-to, FAQ, concept/wiki entry, learning/note, documentation page.

2) Locate destination

  • Pick the correct database using reference/*-database.md guides; confirm required properties (title, tags, owner, status, date, relations).

  • If multiple candidate databases, ask the user which to use; otherwise, create in the primary wiki/documentation DB.

3) Extract and structure

  • Extract facts, decisions, actions, and rationale from the conversation.

  • For decisions, record alternatives, rationale, and outcomes.

  • For how-tos/docs, capture steps, pre-reqs, links to assets/code, and edge cases.

  • For FAQs, phrase as Q&A with concise answers and links to deeper docs.

4) Create/update in Notion

  • Use Notion:notion-create-pages with the correct data_source_id; set properties (title, tags, owner, status, dates, relations).

  • Use templates in reference/ to structure content (section headers, checklists).

  • If updating an existing page, fetch then edit via Notion:notion-update-page.

5) Link and surface

  • Add relations/backlinks to hub pages, related specs/docs, and teams.

  • Add a short summary/changelog for future readers.

  • If follow-up tasks exist, create tasks in the relevant database and link them.

References and examples

  • reference/ — database schemas and templates (e.g., team-wiki-database.md, how-to-guide-database.md, faq-database.md, decision-log-database.md, documentation-database.md, learning-database.md, database-best-practices.md).

  • examples/ — capture patterns in practice (e.g., decision-capture.md, how-to-guide.md, conversation-to-faq.md).

Files

examples/decision-capture.md

Example: Decision Record Capture

User Request: "Document our decision to move from REST to GraphQL API"

Workflow

1. Extract Decision from Context

From conversation, identified:

  • Decision: Migrate customer-facing API from REST to GraphQL

  • Context: REST endpoints becoming unwieldy (50+ endpoints)

  • Alternatives: Keep REST, try gRPC, adopt GraphQL

  • Rationale: Better client experience, type safety, single endpoint

2. Find Decision Log
Notion:notion-search
query: "architecture decisions" or "ADR"

Found: "Architecture Decision Records" database

3. Fetch Schema
Notion:notion-fetch

Got properties: Decision (title), Date, Status, Domain, Impact, Deciders, Stakeholders

4. Create Decision Record
Notion:notion-create-pages
parent: { data_source_id: "decision-log-collection-id" }
pages: [{
  properties: {
    "Decision": "Migrate to GraphQL API",
    "date:Date:start": "2025-10-16",
    "date:Date:is_datetime": 0,
    "Status": "Accepted",
    "Domain": "Architecture",
    "Impact": "High"
  },
  content: "[Full decision record with context, rationale, alternatives...]"
}]

Content sample:

# Migrate Customer-Facing API to GraphQL

## Context
Our REST API has grown to 50+ endpoints with inconsistent patterns. Frontend and mobile teams request new endpoints frequently, leading to:
- API bloat and maintenance burden
- Over-fetching/under-fetching data
- Slow iteration on client features
- Inconsistent error handling

## Decision
Migrate customer-facing API from REST to GraphQL while maintaining REST for internal services.

## Rationale
**Why GraphQL**:
- Clients fetch exactly what they need (no over/under-fetching)
- Single endpoint, self-documenting schema
- Type safety with code generation
- Better developer experience
- Industry standard for client-facing APIs

## Options Considered

### Option A: Keep REST (Status Quo)
**Pros**: No migration cost, team familiar
**Cons**: Doesn't solve core problems, maintenance keeps growing
**Decision**: Rejected - doesn't address root cause

### Option B: gRPC
**Pros**: Performance, strong typing
**Cons**: Browser support issues, steeper learning curve, not designed for client-facing
**Decision**: Rejected - better for internal services

### Option C: GraphQL
**Pros**: Solves over/under-fetching, great DX, strong ecosystem
**Cons**: Learning curve, caching complexity, migration effort
**Decision**: **Accepted**

## Consequences

**Positive**:
- Faster frontend/mobile development
- Reduced API maintenance burden
- Better type safety and tooling
- Single endpoint simplifies deployment

**Negative**:
- 3-4 month migration timeline
- Team needs training
- Need to solve caching strategy
- Monitoring/debugging patterns different

## Implementation Plan
1. GraphQL server setup (Apollo Server)
2. Schema design workshop
3. Gradual migration (start with new features)
4. Dual-run period (both REST and GraphQL)
5. Deprecate old REST endpoints

**Timeline**: Start Q4 2025, complete Q1 2026
**Owner**: Backend team lead

## Success Metrics
- API response times improve 30%
- Client fetch efficiency (less data transferred)
- Reduced new endpoint requests
- Developer satisfaction scores
5. Make Discoverable

Added link from Architecture Wiki and notified team in Slack.

Key Success Factors

  • Captured decision while context fresh

  • Documented alternatives considered

  • Included both pros and cons

  • Clear implementation plan

  • Saved to decision log for future reference

  • Made discoverable for team

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
OpenAI
Publisher
JetBrains (Official)
Distributed by
JetBrains
Source
JetBrains/skills · notion-knowledge-capture · commit 3f53782
Licence
MIT License (redistribution allowed)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by OpenAI or JetBrains.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download notion-knowledge-capture.zip